---
name: greenwood
description: >
  Modern survival analysis for Python: Narwhals-native, R-validated, beautifully visualized. Use when writing Python code that uses the greenwood package.
license: MIT
compatibility: Requires Python >=3.10.
---

# greenwood

Modern survival analysis for Python: Narwhals-native, R-validated, beautifully visualized.

## Installation

```bash
pip install greenwood
```

## API overview

### The response

The Surv object, the spine of every analysis.

- `Surv`: A validated time-to-event response for survival analysis
- `CensoringType`: The censoring flavor of a `Surv` response

### Non-parametric estimators

Kaplan-Meier survival and Nelson-Aalen cumulative hazard.

- `KaplanMeier`: Kaplan-Meier product-limit estimator of the survival function
- `NelsonAalen`: Nelson-Aalen estimator of the cumulative hazard

### Regression

Cox proportional hazards and parametric AFT models.

- `CoxPH`: Cox proportional hazards model
- `CoxNet`: Elastic-net penalized Cox proportional hazards model
- `ZPHResult`: Proportional-hazards test results (Grambsch-Therneau)
- `AFT`: Parametric accelerated failure time model
- `RoystonParmar`: Royston-Parmar flexible parametric survival model (proportional hazards scale)

### Competing risks & multi-state

Cumulative incidence, the Fine-Gray model, and multi-state transition probabilities.

- `AalenJohansen`: Aalen-Johansen estimator of cumulative incidence functions for competing risks
- `FineGray`: Fine-Gray subdistribution hazard model for a competing-risks endpoint
- `MultiState`: Aalen-Johansen estimator of multi-state transition and occupancy probabilities

### Group comparisons

The log-rank test, trend tests for ordered groups, the G-rho (Fleming-Harrington) family, and restricted mean survival time (RMST) comparisons.

- `logrank_test`: Compare survival across groups using the weighted log-rank (G-rho) test
- `trend_test`: Test for linear trend across ordered groups using the log-rank test family
- `pairwise_logrank_test`: Pairwise log-rank tests for all group pairs with multiple-comparison correction
- `TestResult`: The outcome of a log-rank group comparison test
- `rmst_test`: Test for equality of RMST across two or more groups
- `rmst_diff`: Compute RMST difference between two groups with confidence interval
- `pairwise_rmst_test`: Pairwise RMST tests for all group pairs with multiple-comparison correction
- `RMSTResult`: Results of an RMST comparison test or difference calculation
- `logrank_n_events`: Number of events needed for the log-rank test to reach a target power
- `logrank_power`: Power of the log-rank test given the number of observed events
- `logrank_sample_size`: Total sample size needed for the log-rank test to reach a target power

### Prediction performance

Concordance and the IPCW Brier score.

- `concordance_index`: Harrell's concordance index: discrimination of risk scores against observed survival
- `brier_score`: IPCW (Graf) Brier score of predicted survival probabilities at specified times
- `integrated_brier_score`: Integrated (time-averaged) Brier score across multiple time points
- `cross_validate`: Evaluate a survival model's out-of-sample performance using k-fold cross-validation

### Visualization

plotnine survival curves and aligned numbers-at-risk tables.

- `plot_survival`: Plot Kaplan-Meier survival curve(s) with Altair
- `risk_table`: Return the numbers-at-risk table as a standalone Altair chart

### Core kernel

The risk-set / event-table tabulation shared by KM, log-rank, and Cox.

- `EventTable`: Per-time risk-set tabulation (optionally within strata)
- `event_table`: Tabulate the event history: risk sets and events at each observed time

## Resources

- [Full documentation](https://rich-iannone.github.io/greenwood/)
- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs
- [Source code](https://github.com/rich-iannone/greenwood)